Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey

DLNet: Accurate segmentation of green fruit in obscured environments

o achieve more accurate recognition and segmentation of obscured fruit in natural orchard environments, DLNet model is proposed. The model is improved for the more challenging problem of segmenting overlapping fruit from homochromatic backgrounds without considering various damages. This approach is tantamount to construct the detection network RS-RFP and the segmentation network DLNet. RS-RFP extends Full Convolutional One-Stage Object Detection (FCOS). Specifically, Feature Pyramid Network (FPN) by adding Gaussian non-local attention mechanism to build Refined Pyramid Network (RFP) for refining semantic features generated continuously by Residual Network (ResNet) and FPN. The DLNet segmentation framework is composed of a dual-layer Graph Attention Networks (GAT) layer is constructed to model the image as two overlapping layers, where the top GAT layer detects the occluded object (occluded) and the bottom GAT layer infers the partially occluded instance (occlude). Display modeling of the two-layer structure occlusion relationship can naturally the boundaries between the occluded and occlude instances and consider their interactions. The experimental results show that the method outperforms earlier segmentation models and achieves metric values of 80.9% and 81.2% for Average Precision (AP) box and AP mask respectively. In a reasonable running time, it meets the requirements of accuracy and robustness for picking robots and provides a reference for segmentation of other fruits and vegetables

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19

Immunoglobulin G Locus Events in Soft Tissue Sarcoma Cell Lines

Recently immunoglobulins (Igs) have been found to be expressed by cells other than B lymphocytes, including various human carcinoma cells. Sarcomas are derived from mesenchyme, and the knowledge about the occurrence of Ig production in sarcoma cells is very limited. Here we investigated the phenomenon of immunoglobulin G (IgG) expression and its molecular basis in 3 sarcoma cell lines. The mRNA transcripts of IgG heavy chain and kappa light chain were detected by RT-PCR. In addition, the expression of IgG proteins was confirmed by Western blot and immunofluorescence. Immuno-electron microscopy localized IgG to the cell membrane and rough endoplasmic reticulum. The essential enzymes required for gene rearrangement and class switch recombination, and IgG germ-line transcripts were also identified in these sarcoma cells. Chromatin immunoprecipitation results demonstrated histone H3 acetylation of both the recombination activating gene and Ig heavy chain regulatory elements. Collectively, these results confirmed IgG expression in sarcoma cells, the mechanism of which is very similar to that regulating IgG expression in B lymphocytes

Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia

Cool executive dysfunction is a crucial feature in people living with schizophrenia which is related to cognition impairment and the severity of the clinical symptoms. Based on electroencephalogram (EEG), our current study explored the change of brain network under the cool executive tasks in individuals living with schizophrenia before and after atypical antipsychotic treatment (before_TR vs. after_TR). 21 patients with schizophrenia and 24 healthy controls completed the cool executive tasks, involving the Tower of Hanoi Task (THT) and Trail-Marking Test A-B (TMT A-B). The results of this study uncovered that the reaction time of the after_TR group was much shorter than that of the before_TR group in the TMT-A and TMT-B. And the after_TR group showed fewer error numbers in the TMT-B than those of the before_TR group. Concerning the functional network, stronger DMN-like linkages were found in the before_TR group compared to the control group. Finally, we adopted a multiple linear regression model based on the change network properties to predict the patient’s PANSS change ratio. Together, the findings deepened our understanding of cool executive function in individuals living with schizophrenia and might provide physiological information to reliably predict the clinical efficacy of schizophrenia after atypical antipsychotic treatment